{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mk = [\".\",\",\",\"o\",\"v\",\"^\",\">\",\"<\",\"1\",\"2\",\"3\",\"4\",\"8\",\"s\",\"p\",\"*\",\n",
    "     \"h\",\"H\",\"+\",\"x\",\"D\",\"d\",\"|\",\"_\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7))\n",
    "for i in range(1,len(mk)+1):\n",
    "    plt.scatter(i,0,s=200, marker=mk[i-1])\n",
    "    if i % 2 ==0:\n",
    "        plt.text(i,-0.0025,s=mk[i-1],fontsize=20)\n",
    "    else:\n",
    "        plt.text(i,0.0025,s=mk[i-1],fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "mk = [\"$\\clubsuit$\",\"$\\Gamma$\",\"$\\Delta$\", \"$\\Theta$\", \"$\\Lambda$\", \"$\\Xi$\", \n",
    "      \"$\\Pi$\",\"$\\Sigma$\",\"$\\Phi$\",\"$\\Psi$\", \"$\\Omega$\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,7))\n",
    "for i in range(1,len(mk)+1):\n",
    "    plt.scatter(i,0,s=300, marker=mk[i-1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
